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The detection and quantification of heptafluoroisobutyronitrile(C4F7N) and its decomposition products by infrared spectroscopy and chemometrics
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 233 (2020) 118161
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Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy journal homepage: www.elsevier.com/locate/saa
The detection and quantification of heptafluoroisobutyronitrile(C4F7N) and its decomposition products by infrared spectroscopy and chemometrics Xiaoxing Zhang a, Yin Zhang b,⁎, Siyuan Zhou b, Zhuo Wei b, Yi Wang a, Yufei Wang a a b
Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, China School of Electrical and Automation, Wuhan University, Wuhan, China
a r t i c l e
i n f o
Article history: Received 19 December 2019 Received in revised form 4 February 2020 Accepted 16 February 2020 Available online 19 February 2020 Keywords: Alternative gas for SF6 C4F7N decomposition products Quantitative analysis Infrared spectroscopy
a b s t r a c t As an excellent alternative gas for sulfur hexafluoride(SF6), heptafluoroisobutyronitrile(C4F7N) has received widespread attention. C4F7N gas mixture has the potential to be applied to gas-insulated electrical equipment due to its good insulation properties. Quick and easy quantification of the gas mixture and its decomposition products has great significance. In this paper, the infrared spectroscopy is used to detect the three decomposition products of hexafluoropropene(C3F6), carbon monoxide(CO), and carbonyl fluoride(COF2). Combining chemometrics, a partial least squares(PLS) analysis model of C4F7N and its decomposition products is established. Quantitative analysis of CO, C3F6 and C4F7N is achieved by infrared spectroscopy and chemometrics. The research provides new ideas for the decomposition products detection and future online monitoring of alternative gasinsulated equipment. © 2020 Elsevier B.V. All rights reserved.
1. Introduction Sulfur hexafluoride(SF6) is widely used in various gas-insulated electrical equipment due to its outstanding insulation and arc extinguishing performance. However, the strong greenhouse effect of SF6 makes people have to find a more environmentally friendly, safe and reliable gas to replace it [1, 2, 26, 27]. At present, heptafluoroisobutyronitrile(C4F7N) has received extensive attention as an excellent alternative gas for SF6. In recent years, more and more researches on C4F7N gas mixture have been focused on its physical and chemical properties, insulation and arc extinguishing performance, decomposition characteristics and material compatibility [3–6], which has proved its potential to replace SF6 in various medium and high voltage gas-insulated equipment. At the same time, electrical equipment with the C4F7N gas mixture has gradually entered the stage of promotion and application. Current transformers, combined current and voltage transformer with C4F7N-CO2 gas mixture have also been successfully developed [7]. With the gradual maturity of alternative gas-insulated equipment, to ensure the safe and stable operation of the equipment, effective fault
⁎ Corresponding author. E-mail address: [email protected] (Y. Zhang).
https://doi.org/10.1016/j.saa.2020.118161 1386-1425/© 2020 Elsevier B.V. All rights reserved.
diagnosis and condition monitoring of the equipment is indispensable. The mixing ratio detection and leak detection of C4F7N gas mixture through ultraviolet spectroscopy and infrared spectroscopy have been reported [8,9]. These methods can be performed on the equipment that is newly charged with gas and under stable operating conditions. The equipment faults, such as leakage, material adsorption, etc. which leads to a decrease in the internal primary gas (C4F7N) content, can be judged. However, in the long-term operation process, due to the unavoidable potential insulation defects during manufacturing, installation, etc., it will directly cause partial discharge and partial overheating inside the equipment. It is similar to SF6 gas-insulated electrical equipment [10,11], the insulation faults will cause the gas mixture to decompose. Through the concentration information of the decomposition products inside the equipment, the insulation fault can be effectively analysed [12]. Therefore, quick and easy decomposition products detection is necessary for the stable operation of the equipment. At present, the quantification of the decomposition products of the C4F7N gas mixture is mainly carried out in the study of the decomposition characteristics. The quantitative analysis of the decomposition products is performed by gas chromatography–mass spectrometry (GC–MS). GC–MS is mainly used for laboratory testing. It is expensive and complicated to operate, which is not suitable for on-site detection or online monitoring. Infrared spectroscopy is a non-destructive, quick and easy detection method, which is widely used in food safety, environmental monitoring,
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X. Zhang et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 233 (2020) 118161
and medicine [13–15]. It can be used for qualitative analysis of the molecular structure and quantitative detection. Due to the infrared spectroscopy has the ability of online monitoring or portable detection, it has some applications in the field of detection of SF6 decomposition products [16–18]. So it is also suitable for the detection of C4F7N and its decomposition products. The infrared spectrum of the sample contains overall complex data information. Changes in the position and intensity of bands in the spectrum would be associated with the changes in the chemical composition of a sample [19]. For C4F7N gas itself, it has strong infrared spectral absorption and wide range of wavelengths, covering almost 400–4000 cm−1 [8]. Moreover, the concentration of C4F7N decomposition products is usually in the ppm level, which needs to be detected by a long optical path gas cell. The long path gas cell further amplifies the infrared absorption spectrum of the C4F7N gas itself, which will cause its overlap with the spectrum of the decomposition products. Therefore, it is necessary to analyse and process spectral data by means of chemometrics. Chemometrics processes chemical data by applying the principles and methods of mathematics, statistics, and computer technology to maximize the extraction of valid information from chemical measurement data [20,21]. By combining infrared spectroscopy with chemometrics to establish a reasonable analysis model, the gas mixture concentration information can be accurately obtained. In summary, in this paper, the experiment platform of overheating decomposition is used for simulating the decomposition of C4F7N gas under overheating faults. Infrared spectrum detection is performed on the gas mixture through a long path gas cell. The decomposition products that can be detected by the gas cell is determined. In the process of quantitative analysis, since there is no large sample library, 100 sets of the gas mixture with decomposition products are prepared as the sample library randomly. A partial least squares(PLS) analysis model is established using 100 sets of infrared spectral data samples combined with chemometrics. Quantitative analysis of C4F7N and its decomposition products is carried out. 2. Method 2.1. Overheating experiment In the experiment, C4F7N gas is provided by 3 M Company, and the purity is not b99.2%. N2, CO2 and standard gases of various
decomposition products are purchased from Wuhan Newradar Company. The overheating experiment platform built in the laboratory is shown in Fig. 1. The chamber is made of stainless steel, and the upper cover is fixed with screws. A heating rod and a temperature sensor are installed in the chamber. The temperature is controlled by the power supply and temperature control device. An electronic barometer is connected to the gas chamber. The C4F7N gas mixture enters through the inlet, and the sample bag is used for gas collection. 2.2. Infrared spectrum detection Fourier infrared spectrometer (Shimadzu IRTracer-100, spectral range: 350–7800 cm−1; IR source: high-energy mid-far infrared ceramic light source, air-cooled; beam splitter: mid-IR germaniumplated KBr beam splitter; detector: temperature-controlled, highly sensitive DLATGS detector) combined with a 4 m long optical path gas cell (the cell body is made of stainless steel, the mirror is made of copperpolished gold-plated material, the reflectors are made of polished copper and are gold-plated on the surface, the optical lens is zinc selenide, its light transmission band is 400–5200 cm−1) are used for infrared spectrum detection. The spectral resolution is chosen to be 2 cm−1 in the experiment. Take the average of ten measurements for each detection as spectral data. Standard gas and sample gas mixtures of different concentrations are prepared by the dynamic gas distribution instrument (Tunkon Electrical, GC500, the maximum output flow rate: 3000 mL/min, the instrument accuracy: ≤± 1% F.S, and the maximum dilution ratio: 300:1). The gas mixture sample data are randomly generated by Excel. According to the sample data, 100 sets of gas mixture samples are prepared. 2.3. Calculation: PLS The chemometric method used in this paper is PLS. PLS is the representative of the multivariate calibration technique in chemometrics. It has been widely used in chemometrics, especially in the multivariate calibration analysis of near-infrared spectroscopy. PLS is a regression modeling method of multiple dependent variable Y to multiple independent variable X. In the process of establishing regression, this algorithm considers not only extracting the principal components from Y and X as much as possible but also the maximum correlation between the principal components extracted from X and Y [22]. PLS is the product of the combination of three basic algorithms:
Fig. 1. The overheating experiment platform.
X. Zhang et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 233 (2020) 118161
principal component analysis (PCA), canonical correlation analysis (CCA), and multiple linear regression. Firstly, the spectral matrix X and the concentration matrix Y are decomposed. The model is: f X
Y ¼ UQ T þ EY ¼
uk qTk þ EY
ð1Þ
k¼1
X ¼ TP T þ EX ¼
f X
t k pTk þ EX
ð2Þ
k¼1
where tk (nx1) is the score of the k-th main factor of the absorbance matrix X; pk (1xm) is the load of the k-th main factor of the absorbance matrix X; uk (nx1) is the score of the k-th main factor of the concentration matrix Y; qk (1xp) is the load of the k-th main factor of the concentration matrix Y; f is the main factor. That is, T and U are the scoring matrices of the X and Y, P and Q are the load matrices of the X and Y, and EX and EY are the fitting residual matrices of the X and Y. The second step of PLS is to perform linear regression on T and U: U ¼ TB
ð3Þ
−1 B ¼ TT T TT Y
ð4Þ
In the prediction, according to the matrix P, finding the score matrix Tunknown of the spectral matrix Xunknown of the unknown sample, and then predicting the concentration value by the following formula: Y unknown ¼ T unknown BQ
ð5Þ
PLS combines the matrix decomposition and regression into one step, that is, the decomposition of matrix X and matrix Y is performed simultaneously. And the information of matrix Y is introduced into the matrix X decomposition process. Before calculating each new principal component, the score matrix T is exchanged with the score matrix U, so that the principal component of X is directly related to Y. The specific algorithm is as follows [23], for the correction process, ignoring the residual matrix E, when the number of main factors is 1: for X = tpT, multiply the left side by tT: pT = tTX/(tTt), multiply the right side by p: t = Xp/(pTp). For Y = uqT, multiply the left side by uT: qT = uTY/(uTu), and divide both sides by qT: u = Y/qT. 1) Find the weight vector w of the absorbance matrix X: take a certain column of the concentration matrix Y as the initial iteration value of u, and replace t with u to calculate w. The equation is: X = uwT. The solution is: wT = uTX/(uTu); 2) Normalize the weight vector w, wT = wT/||wT||; 3) Find the factor score t of the absorbance matrix X, and calculate t from the normalized w. The equation is: X = twT. The solution is: t = Xw/(wTw); 4) Find the load q of the concentration matrix Y and calculate q with t instead of u. The equation is Y = tqT, and the solution is qT = tTY/(tTt); 5) Normalize the load q, qT = qT/||qT||; 6) Find the factor score u of the concentration matrix Y and calculate u from qT. The equation is: Y = uqT. The solution is: u = Yq/(qTq); 7) Then use u instead of t to return to step 1) to calculate wT. Calculate tnew from wT. Repeat this iteration. If t has converged (||tnewtold|| ≤ 10−6||tnew||), go to step 8), operation, otherwise return to step 1); 8) The load vector p of the absorbance matrix X is obtained from t after convergence. The equation is: X = tpT, and its solution is: pT = tTY/(tTt); 9) Normalize the load p: pT = pT/||pT||; 10) Normalized factor score t for X: t = t||p||; 11) Normalized weight vector w: w = w||p||; 12) Calculate the intrinsic relationship b between t and u. b = uTt/ (tTt);
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13) Calculate the residual matrix E. EX = X-tpT, EY=Y-btqT; 14) Replace X with EX, Y with EY, and return to step 1), and so on to find the w, t, p, u, q, b of each main factor of X, Y. Use the cross test method to determine the optimal number of principal factors f. The prediction process for the unknown sample xunknown is as follows: 1) Let h = 0 and yunknown = 0; 2) Let h = h + 1 and calculate th = xunknownwTh, yunknown = yunknown + bhthqTh, and xunknown = xunknown-thpTh; 3) If h b f, go to step 2), otherwise, stop the calculation, and the final yunknown is the predicted value. 3. Results and discussion 3.1. Detectable decomposition products Fig. 2 shows the infrared spectrum of 4% C4F7N-CO2 gas mixture before and after overheating, 500 °C, 70 kPa and 12 h. The black line in the figure shows the spectrum before overheating and the red line shows the spectrum after overheating. It can be seen that the infrared spectrum of C4F7N gas mixture has changed before and after overheating, and the absorbance in some bands has increased significantly, indicating that decomposition products have been generated. Especially at 2100–2200 cm−1, there are obvious sawtooth peaks, which can be inferred to be the infrared spectrum of carbon monoxide(CO). In addition, when the band is larger than 2200 cm−1, the saturation phenomenon of the absorption spectrum occurs. This is due to the high concentration and strong absorption of the gas mixture in this band. Therefore, only the unsaturated band is analysed, and the product information in the saturated band cannot be analysed. The infrared spectrum before and after overheating is calculated by subtractive spectrum, that is, subtract the original spectrum before overheating from the spectrum after overheating(the black line is subtracted from the red line in Fig. 2). The subtractive spectrum thus obtained only shows the spectral information of the decomposition products produced after overheating. The subtractive spectrum and the standard infrared spectrum of known decomposition products are shown in Fig. 3. It shows that CO, hexafluoropropene(C3F6) and carbonyl fluoride(COF2) can be detected by infrared spectroscopy with 4 m long path gas cell. Most of the formation of CO is due to the reaction of CO2 with the C element in stainless steel under
Fig. 2. The infrared spectrum before and after overheating experiment: 500 °C, 70 kPa and 12 h, the spectral resolution of 2 cm−1.
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X. Zhang et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 233 (2020) 118161
Fig. 3. The subtractive spectrum and the standard infrared spectrum of known decomposition products.
overheating. The specific generation process of C3F6 and COF2 is shown in reference [24]. After that, the C4F7N gas mixture overheating experiment is performed at different temperatures. The temperature is set to five gradients of 350 °C, 400 °C, 450 °C, 500 °C and 550 °C, the gas mixing ratio is 6%, the pressure is 0.15 MPa, and the time is 12 h. The change in absorbance of each decomposition product according to the subtractive spectrum is shown in Table 1. The generation trend of each decomposition product with temperature change can be roughly obtained. Through the subtractive spectrum calculation, the absorbance of the three decomposition products can be observed, so that the decomposition products can be qualitatively judged. However, in the infrared band where the decomposition products can be detected, there is an overlap between C4F7N and the decomposition products, so the concentration calculation cannot be made directly using Lambert's law. Since the process of calculating the subtractive spectrum is rough and the concentration of C4F7N may decrease during the decomposition process, the concentration inversion using the subtractive spectrum cannot accurately obtain the concentration information of the decomposition products. Therefore, the concentration calculation needs to employ chemometric methods.
3.2. Quantitative analysis by PLS 3.2.1. Sample data acquisition Chemometrics is the application of disciplines such as mathematics, statistics, and computer science in analytical chemistry. Through a large number of data samples, a reliable analysis model is established to effectively predict the concentration of the unknown sample. Conventional chemometric methods usually already have a large amount of
Table 1 The Change in absorbance of the three decomposition products at different temperatures. Temperature (°C)
CO
COF2
C3F6
350 400 450 500 550
0.067 0.094 0.255 0.293 0.397
0.002 0.002 0.129 0.103 0.226
0.399 0.813 1.386 0.633 1.275
experimental sample data. But for this paper, there is not a large amount of sample data available to build an analysis model. Therefore, to detect the concentration of 6% C4F7N-CO2 gas mixture and its decomposition products at different overheating temperatures. According to the requirements, 100 sets of samples are prepared. Based on the samples, an analytical model is established to effectively predict the concentration of C4F7N and decomposition products. Because COF2 is highly toxic, it will also cause corrosion to the gas paths of various instruments and equipment, and no manufacturers have been found to sell this gas. Therefore, quantitative analysis is targeted at three gases, C4F7N, C3F6, and CO. When preparing samples, the concentration range of the sample needs to be considered. With reference to the absorbance of each decomposition product at different temperatures in Section 3.1, preparing different concentrations of standard gas for infrared spectrum detection. The standard gas spectrum is compared with the infrared spectrum after the overheating experiment to determine the maximum concentration of the decomposition products. Its infrared absorbance should be greater than that of the gas after the overheating experiment, but it should not exceed too much. The final sample concentration range: C 3 F6 is 5–450 ppm, CO is 5–1800 ppm, and C 4F 7N is 5–6%. The random data generation method in Excel is used to randomly generate 100 sets of gas mixture concentration data, and the gas mixture is prepared by the dynamic gas distribution instrument. Then the infrared spectrum of the gas mixture is obtained as samples. 3.2.2. Sample processing Establishing the PLS model requires selecting a suitable band and preprocessing the infrared spectrum. Because the absorption peak of C3F6 is about 1800 cm−1, the detection band of C3F6 can be selected in the range of 1700–1900 cm−1. Similarly, the detection band of CO is 2050–2200 cm−1. Because there is infrared absorption of C4F7N in these two bands, the concentration of C4F7N is predicted in each analysis model. The band with higher fitting degree and more accurate prediction is used for C4F7N concentration detection. Fig. 4 shows the infrared spectrum of 50 randomly selected infrared spectrum samples in the 1700–1900 cm−1. The main methods of preprocessing the infrared spectrum of the selected band: (1) First derivative; (2) Savitzky-Golay filter; (3) First derivative + Savitzky-Golay filter; (4) First derivative + Norris Derivative filter. Among them, Savitzky-Golay filter smoothing sets a polynomial value range of 7 and an order of 3; Norris Derivative filter segment length is 5 and segment spacing is 5. 3.2.3. Concentration prediction 70 sets of samples are selected as the sample set and 30 sets of samples are used as the prediction set. An analysis model is established, and the results obtained by different preprocess methods are shown in Table 2. The optimal spectral preprocess method is determined according to the correlation coefficient R, the root mean square error of correction (RMSEC) and root mean square error of prediction (RMSEP). It is generally believed that the closer the correlation coefficient R is to 1 and the root mean square error is closer to 0, the better the model is. Therefore, for the detection of C3F6, the best detection results can be obtained by directly quantifying the raw spectrum without processing. For the detection of CO, it is necessary to preprocess through the SavitzkyGolay filter. For the detection of C4F7N, the detection effect of the band overlapping with CO is better. Finally, the best detection scheme in Table 2 is used to quantitative analysis C4F7N, C3F6, and CO in the overheating experiment. The quantitative model of each component is shown in Fig. 5. The detection results are shown in Table 3. It can be seen that the CO concentration has a significant decrease at 500 °C, which has the same regularity as the absorbance analysis of C 3 F6 and COF2 in Section 3.1. However, the phenomenon is not observed by the
X. Zhang et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 233 (2020) 118161
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Fig. 4. 50 infrared spectral sample data in the 1700–1900 cm−1.
absorbance trend, which indicates that the rough subtractive spectrum is only suitable for qualitative judgment. The concentration of C4 F 7N is 6.35% at 350 °C due to the partial pressure method used in the inflation of the heating chamber, which may have some errors. As the temperature increased, the concentration of C 4F 7N generally decreased, but the decrease is small, indicating that the decomposition caused by high temperature is slower. The formation of the main decomposition product CO is observed to start at a temperature of approximately 650 °C in the thermal stability analysis of trace C4F7N [25]. It is similar to the results in this paper: with increasing temperature, and concentration can exceed 1000 ppm. In this paper, because the heating time is long and the concentration of C4F7N is higher, the possible decomposition temperature is lower and the concentration is higher. In addition, the results of C4F7N-N2 overheating decomposition experiment shows that the concentration of C3F6 is significantly higher than other decomposition products [24]. The highest concentration is close to 250 ppm, which indicates that C3F6 can be used as a feature parameter for overheating fault assessment.
In general, infrared spectroscopy and chemometric can be well used to predict the concentration of the decomposition products. It can not only contribute to the study of C4F7N decomposition characteristics but also can be applied to future equipment monitoring. Besides, as two main and detectable decomposition products, CO and C3F6 are biotoxic. Quick and easy quantification of the two decomposition products also has great significance for ensuring personal safety. 4. Conclusion In this paper, the C4F7N-CO2 gas mixture overheating decomposition is analysed by infrared spectroscopy. It is found that CO, C3F6, and COF2 can be effectively detected using the 4 m long optical path gas cell. Based on 100 randomly prepared samples of the gas mixture with decomposition products, the PLS analysis model is established to determine the optimal detection bands and preprocess method for CO, C 3F 6 and C4 F 7N. The concentrations of the three gases at different temperatures are predicted. The research
Table 2 Modeling results of different components under different preprocess method. Component
Band −1
C3F6
1776.15–1821.11 cm
C4F7N
1776.15–1821.11 cm−1
2102.03–2152.17 cm−1
CO
2102.03–2152.17 cm−1
Preprocess
R
RMSEC
RMSEP
Without preprocess First derivative Savitzky-Golay filter First derivative + Savitzky-Golay filter First derivative + Norris Derivative filter Without preprocess First derivative Savitzky-Golay filter First derivative + Savitzky-Golay filter First derivative + Norris Derivative filter Without preprocess First derivative Savitzky-Golay filter First derivative + Savitzky-Golay filter First derivative + Norris Derivative filter Without preprocess First derivative Savitzky-Golay filter First derivative + Savitzky-Golay filter First derivative + Norris Derivative filter
0.9974 0.9966 0.9971 0.9942 0.9963 0.7884 0.8102 0.8143 0.8025 0.7836 0.9663 0.9638 0.9624 0.9634 0.9563 0.9955 0.9985 0.9984 0.9984 0.9978
9.94 10.6 10.3 14.1 10.9 0.196 0.187 0.185 0.190 0.198 0.077 0.080 0.081 0.080 0.087 52.3 30.4 30.9 31.6 36.5
9.48 11.7 9.75 14.8 10.6 0.198 0.282 0.195 0.269 0.214 0.100 0.118 0.104 0.110 0.112 50.0 48.9 41.2 46.2 35.0
6
X. Zhang et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 233 (2020) 118161
Table 3 Concentrations prediction based on PLS model at different temperatures.
Validation Calibration
Predicted value (ppm)
Temperature (°C)
CO (ppm)
C3F6 (ppm)
C4F7N (%)
350 400 450 500 550
61.73 103.04 517.85 409.46 1117.74
113.36 253.28 321.02 186.48 330.85
6.35 6.32 6.28 6.02 5.99
R=0.9974
CRediT authorship contribution statement
C3F6
Xiaoxing Zhang: Conceptualization, Validation, Resources, Writing review & editing, Supervision. Yin Zhang: Conceptualization, Methodology, Software, Writing - original draft, Supervision. Siyuan Zhou: Software, Formal analysis. Zhuo Wei: Data curation, Investigation.Yi Wang : Data curation, Investigation. Yufei Wang: Data curation, Investigation.
Actual value (ppm)
Declaration of competing interest
Validation Calibration
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Predicted value (ppm)
References
R=0.9985
CO
Actual value (ppm)
Validation Calibration
Predicted value (%)
R=0.9663
C4F7N
Actual value (%) Fig. 5. The quantitative model of each component.
provides new ideas for the monitoring of alternative gas-insulated equipment based on infrared spectroscopy in the future, which has good application potential.
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The detection and quantification of heptafluoroisobutyronitrile(C4F7N) and its decomposition products by infrared spectroscopy and chemometrics Xiaoxing Zhang a, Yin Zhang b,⁎, Siyuan Zhou b, Zhuo Wei b, Yi Wang a, Yufei Wang a a b
Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, China School of Electrical and Automation, Wuhan University, Wuhan, China
a r t i c l e
i n f o
Article history: Received 19 December 2019 Received in revised form 4 February 2020 Accepted 16 February 2020 Available online 19 February 2020 Keywords: Alternative gas for SF6 C4F7N decomposition products Quantitative analysis Infrared spectroscopy
a b s t r a c t As an excellent alternative gas for sulfur hexafluoride(SF6), heptafluoroisobutyronitrile(C4F7N) has received widespread attention. C4F7N gas mixture has the potential to be applied to gas-insulated electrical equipment due to its good insulation properties. Quick and easy quantification of the gas mixture and its decomposition products has great significance. In this paper, the infrared spectroscopy is used to detect the three decomposition products of hexafluoropropene(C3F6), carbon monoxide(CO), and carbonyl fluoride(COF2). Combining chemometrics, a partial least squares(PLS) analysis model of C4F7N and its decomposition products is established. Quantitative analysis of CO, C3F6 and C4F7N is achieved by infrared spectroscopy and chemometrics. The research provides new ideas for the decomposition products detection and future online monitoring of alternative gasinsulated equipment. © 2020 Elsevier B.V. All rights reserved.
1. Introduction Sulfur hexafluoride(SF6) is widely used in various gas-insulated electrical equipment due to its outstanding insulation and arc extinguishing performance. However, the strong greenhouse effect of SF6 makes people have to find a more environmentally friendly, safe and reliable gas to replace it [1, 2, 26, 27]. At present, heptafluoroisobutyronitrile(C4F7N) has received extensive attention as an excellent alternative gas for SF6. In recent years, more and more researches on C4F7N gas mixture have been focused on its physical and chemical properties, insulation and arc extinguishing performance, decomposition characteristics and material compatibility [3–6], which has proved its potential to replace SF6 in various medium and high voltage gas-insulated equipment. At the same time, electrical equipment with the C4F7N gas mixture has gradually entered the stage of promotion and application. Current transformers, combined current and voltage transformer with C4F7N-CO2 gas mixture have also been successfully developed [7]. With the gradual maturity of alternative gas-insulated equipment, to ensure the safe and stable operation of the equipment, effective fault
⁎ Corresponding author. E-mail address: [email protected] (Y. Zhang).
https://doi.org/10.1016/j.saa.2020.118161 1386-1425/© 2020 Elsevier B.V. All rights reserved.
diagnosis and condition monitoring of the equipment is indispensable. The mixing ratio detection and leak detection of C4F7N gas mixture through ultraviolet spectroscopy and infrared spectroscopy have been reported [8,9]. These methods can be performed on the equipment that is newly charged with gas and under stable operating conditions. The equipment faults, such as leakage, material adsorption, etc. which leads to a decrease in the internal primary gas (C4F7N) content, can be judged. However, in the long-term operation process, due to the unavoidable potential insulation defects during manufacturing, installation, etc., it will directly cause partial discharge and partial overheating inside the equipment. It is similar to SF6 gas-insulated electrical equipment [10,11], the insulation faults will cause the gas mixture to decompose. Through the concentration information of the decomposition products inside the equipment, the insulation fault can be effectively analysed [12]. Therefore, quick and easy decomposition products detection is necessary for the stable operation of the equipment. At present, the quantification of the decomposition products of the C4F7N gas mixture is mainly carried out in the study of the decomposition characteristics. The quantitative analysis of the decomposition products is performed by gas chromatography–mass spectrometry (GC–MS). GC–MS is mainly used for laboratory testing. It is expensive and complicated to operate, which is not suitable for on-site detection or online monitoring. Infrared spectroscopy is a non-destructive, quick and easy detection method, which is widely used in food safety, environmental monitoring,
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and medicine [13–15]. It can be used for qualitative analysis of the molecular structure and quantitative detection. Due to the infrared spectroscopy has the ability of online monitoring or portable detection, it has some applications in the field of detection of SF6 decomposition products [16–18]. So it is also suitable for the detection of C4F7N and its decomposition products. The infrared spectrum of the sample contains overall complex data information. Changes in the position and intensity of bands in the spectrum would be associated with the changes in the chemical composition of a sample [19]. For C4F7N gas itself, it has strong infrared spectral absorption and wide range of wavelengths, covering almost 400–4000 cm−1 [8]. Moreover, the concentration of C4F7N decomposition products is usually in the ppm level, which needs to be detected by a long optical path gas cell. The long path gas cell further amplifies the infrared absorption spectrum of the C4F7N gas itself, which will cause its overlap with the spectrum of the decomposition products. Therefore, it is necessary to analyse and process spectral data by means of chemometrics. Chemometrics processes chemical data by applying the principles and methods of mathematics, statistics, and computer technology to maximize the extraction of valid information from chemical measurement data [20,21]. By combining infrared spectroscopy with chemometrics to establish a reasonable analysis model, the gas mixture concentration information can be accurately obtained. In summary, in this paper, the experiment platform of overheating decomposition is used for simulating the decomposition of C4F7N gas under overheating faults. Infrared spectrum detection is performed on the gas mixture through a long path gas cell. The decomposition products that can be detected by the gas cell is determined. In the process of quantitative analysis, since there is no large sample library, 100 sets of the gas mixture with decomposition products are prepared as the sample library randomly. A partial least squares(PLS) analysis model is established using 100 sets of infrared spectral data samples combined with chemometrics. Quantitative analysis of C4F7N and its decomposition products is carried out. 2. Method 2.1. Overheating experiment In the experiment, C4F7N gas is provided by 3 M Company, and the purity is not b99.2%. N2, CO2 and standard gases of various
decomposition products are purchased from Wuhan Newradar Company. The overheating experiment platform built in the laboratory is shown in Fig. 1. The chamber is made of stainless steel, and the upper cover is fixed with screws. A heating rod and a temperature sensor are installed in the chamber. The temperature is controlled by the power supply and temperature control device. An electronic barometer is connected to the gas chamber. The C4F7N gas mixture enters through the inlet, and the sample bag is used for gas collection. 2.2. Infrared spectrum detection Fourier infrared spectrometer (Shimadzu IRTracer-100, spectral range: 350–7800 cm−1; IR source: high-energy mid-far infrared ceramic light source, air-cooled; beam splitter: mid-IR germaniumplated KBr beam splitter; detector: temperature-controlled, highly sensitive DLATGS detector) combined with a 4 m long optical path gas cell (the cell body is made of stainless steel, the mirror is made of copperpolished gold-plated material, the reflectors are made of polished copper and are gold-plated on the surface, the optical lens is zinc selenide, its light transmission band is 400–5200 cm−1) are used for infrared spectrum detection. The spectral resolution is chosen to be 2 cm−1 in the experiment. Take the average of ten measurements for each detection as spectral data. Standard gas and sample gas mixtures of different concentrations are prepared by the dynamic gas distribution instrument (Tunkon Electrical, GC500, the maximum output flow rate: 3000 mL/min, the instrument accuracy: ≤± 1% F.S, and the maximum dilution ratio: 300:1). The gas mixture sample data are randomly generated by Excel. According to the sample data, 100 sets of gas mixture samples are prepared. 2.3. Calculation: PLS The chemometric method used in this paper is PLS. PLS is the representative of the multivariate calibration technique in chemometrics. It has been widely used in chemometrics, especially in the multivariate calibration analysis of near-infrared spectroscopy. PLS is a regression modeling method of multiple dependent variable Y to multiple independent variable X. In the process of establishing regression, this algorithm considers not only extracting the principal components from Y and X as much as possible but also the maximum correlation between the principal components extracted from X and Y [22]. PLS is the product of the combination of three basic algorithms:
Fig. 1. The overheating experiment platform.
X. Zhang et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 233 (2020) 118161
principal component analysis (PCA), canonical correlation analysis (CCA), and multiple linear regression. Firstly, the spectral matrix X and the concentration matrix Y are decomposed. The model is: f X
Y ¼ UQ T þ EY ¼
uk qTk þ EY
ð1Þ
k¼1
X ¼ TP T þ EX ¼
f X
t k pTk þ EX
ð2Þ
k¼1
where tk (nx1) is the score of the k-th main factor of the absorbance matrix X; pk (1xm) is the load of the k-th main factor of the absorbance matrix X; uk (nx1) is the score of the k-th main factor of the concentration matrix Y; qk (1xp) is the load of the k-th main factor of the concentration matrix Y; f is the main factor. That is, T and U are the scoring matrices of the X and Y, P and Q are the load matrices of the X and Y, and EX and EY are the fitting residual matrices of the X and Y. The second step of PLS is to perform linear regression on T and U: U ¼ TB
ð3Þ
−1 B ¼ TT T TT Y
ð4Þ
In the prediction, according to the matrix P, finding the score matrix Tunknown of the spectral matrix Xunknown of the unknown sample, and then predicting the concentration value by the following formula: Y unknown ¼ T unknown BQ
ð5Þ
PLS combines the matrix decomposition and regression into one step, that is, the decomposition of matrix X and matrix Y is performed simultaneously. And the information of matrix Y is introduced into the matrix X decomposition process. Before calculating each new principal component, the score matrix T is exchanged with the score matrix U, so that the principal component of X is directly related to Y. The specific algorithm is as follows [23], for the correction process, ignoring the residual matrix E, when the number of main factors is 1: for X = tpT, multiply the left side by tT: pT = tTX/(tTt), multiply the right side by p: t = Xp/(pTp). For Y = uqT, multiply the left side by uT: qT = uTY/(uTu), and divide both sides by qT: u = Y/qT. 1) Find the weight vector w of the absorbance matrix X: take a certain column of the concentration matrix Y as the initial iteration value of u, and replace t with u to calculate w. The equation is: X = uwT. The solution is: wT = uTX/(uTu); 2) Normalize the weight vector w, wT = wT/||wT||; 3) Find the factor score t of the absorbance matrix X, and calculate t from the normalized w. The equation is: X = twT. The solution is: t = Xw/(wTw); 4) Find the load q of the concentration matrix Y and calculate q with t instead of u. The equation is Y = tqT, and the solution is qT = tTY/(tTt); 5) Normalize the load q, qT = qT/||qT||; 6) Find the factor score u of the concentration matrix Y and calculate u from qT. The equation is: Y = uqT. The solution is: u = Yq/(qTq); 7) Then use u instead of t to return to step 1) to calculate wT. Calculate tnew from wT. Repeat this iteration. If t has converged (||tnewtold|| ≤ 10−6||tnew||), go to step 8), operation, otherwise return to step 1); 8) The load vector p of the absorbance matrix X is obtained from t after convergence. The equation is: X = tpT, and its solution is: pT = tTY/(tTt); 9) Normalize the load p: pT = pT/||pT||; 10) Normalized factor score t for X: t = t||p||; 11) Normalized weight vector w: w = w||p||; 12) Calculate the intrinsic relationship b between t and u. b = uTt/ (tTt);
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13) Calculate the residual matrix E. EX = X-tpT, EY=Y-btqT; 14) Replace X with EX, Y with EY, and return to step 1), and so on to find the w, t, p, u, q, b of each main factor of X, Y. Use the cross test method to determine the optimal number of principal factors f. The prediction process for the unknown sample xunknown is as follows: 1) Let h = 0 and yunknown = 0; 2) Let h = h + 1 and calculate th = xunknownwTh, yunknown = yunknown + bhthqTh, and xunknown = xunknown-thpTh; 3) If h b f, go to step 2), otherwise, stop the calculation, and the final yunknown is the predicted value. 3. Results and discussion 3.1. Detectable decomposition products Fig. 2 shows the infrared spectrum of 4% C4F7N-CO2 gas mixture before and after overheating, 500 °C, 70 kPa and 12 h. The black line in the figure shows the spectrum before overheating and the red line shows the spectrum after overheating. It can be seen that the infrared spectrum of C4F7N gas mixture has changed before and after overheating, and the absorbance in some bands has increased significantly, indicating that decomposition products have been generated. Especially at 2100–2200 cm−1, there are obvious sawtooth peaks, which can be inferred to be the infrared spectrum of carbon monoxide(CO). In addition, when the band is larger than 2200 cm−1, the saturation phenomenon of the absorption spectrum occurs. This is due to the high concentration and strong absorption of the gas mixture in this band. Therefore, only the unsaturated band is analysed, and the product information in the saturated band cannot be analysed. The infrared spectrum before and after overheating is calculated by subtractive spectrum, that is, subtract the original spectrum before overheating from the spectrum after overheating(the black line is subtracted from the red line in Fig. 2). The subtractive spectrum thus obtained only shows the spectral information of the decomposition products produced after overheating. The subtractive spectrum and the standard infrared spectrum of known decomposition products are shown in Fig. 3. It shows that CO, hexafluoropropene(C3F6) and carbonyl fluoride(COF2) can be detected by infrared spectroscopy with 4 m long path gas cell. Most of the formation of CO is due to the reaction of CO2 with the C element in stainless steel under
Fig. 2. The infrared spectrum before and after overheating experiment: 500 °C, 70 kPa and 12 h, the spectral resolution of 2 cm−1.
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Fig. 3. The subtractive spectrum and the standard infrared spectrum of known decomposition products.
overheating. The specific generation process of C3F6 and COF2 is shown in reference [24]. After that, the C4F7N gas mixture overheating experiment is performed at different temperatures. The temperature is set to five gradients of 350 °C, 400 °C, 450 °C, 500 °C and 550 °C, the gas mixing ratio is 6%, the pressure is 0.15 MPa, and the time is 12 h. The change in absorbance of each decomposition product according to the subtractive spectrum is shown in Table 1. The generation trend of each decomposition product with temperature change can be roughly obtained. Through the subtractive spectrum calculation, the absorbance of the three decomposition products can be observed, so that the decomposition products can be qualitatively judged. However, in the infrared band where the decomposition products can be detected, there is an overlap between C4F7N and the decomposition products, so the concentration calculation cannot be made directly using Lambert's law. Since the process of calculating the subtractive spectrum is rough and the concentration of C4F7N may decrease during the decomposition process, the concentration inversion using the subtractive spectrum cannot accurately obtain the concentration information of the decomposition products. Therefore, the concentration calculation needs to employ chemometric methods.
3.2. Quantitative analysis by PLS 3.2.1. Sample data acquisition Chemometrics is the application of disciplines such as mathematics, statistics, and computer science in analytical chemistry. Through a large number of data samples, a reliable analysis model is established to effectively predict the concentration of the unknown sample. Conventional chemometric methods usually already have a large amount of
Table 1 The Change in absorbance of the three decomposition products at different temperatures. Temperature (°C)
CO
COF2
C3F6
350 400 450 500 550
0.067 0.094 0.255 0.293 0.397
0.002 0.002 0.129 0.103 0.226
0.399 0.813 1.386 0.633 1.275
experimental sample data. But for this paper, there is not a large amount of sample data available to build an analysis model. Therefore, to detect the concentration of 6% C4F7N-CO2 gas mixture and its decomposition products at different overheating temperatures. According to the requirements, 100 sets of samples are prepared. Based on the samples, an analytical model is established to effectively predict the concentration of C4F7N and decomposition products. Because COF2 is highly toxic, it will also cause corrosion to the gas paths of various instruments and equipment, and no manufacturers have been found to sell this gas. Therefore, quantitative analysis is targeted at three gases, C4F7N, C3F6, and CO. When preparing samples, the concentration range of the sample needs to be considered. With reference to the absorbance of each decomposition product at different temperatures in Section 3.1, preparing different concentrations of standard gas for infrared spectrum detection. The standard gas spectrum is compared with the infrared spectrum after the overheating experiment to determine the maximum concentration of the decomposition products. Its infrared absorbance should be greater than that of the gas after the overheating experiment, but it should not exceed too much. The final sample concentration range: C 3 F6 is 5–450 ppm, CO is 5–1800 ppm, and C 4F 7N is 5–6%. The random data generation method in Excel is used to randomly generate 100 sets of gas mixture concentration data, and the gas mixture is prepared by the dynamic gas distribution instrument. Then the infrared spectrum of the gas mixture is obtained as samples. 3.2.2. Sample processing Establishing the PLS model requires selecting a suitable band and preprocessing the infrared spectrum. Because the absorption peak of C3F6 is about 1800 cm−1, the detection band of C3F6 can be selected in the range of 1700–1900 cm−1. Similarly, the detection band of CO is 2050–2200 cm−1. Because there is infrared absorption of C4F7N in these two bands, the concentration of C4F7N is predicted in each analysis model. The band with higher fitting degree and more accurate prediction is used for C4F7N concentration detection. Fig. 4 shows the infrared spectrum of 50 randomly selected infrared spectrum samples in the 1700–1900 cm−1. The main methods of preprocessing the infrared spectrum of the selected band: (1) First derivative; (2) Savitzky-Golay filter; (3) First derivative + Savitzky-Golay filter; (4) First derivative + Norris Derivative filter. Among them, Savitzky-Golay filter smoothing sets a polynomial value range of 7 and an order of 3; Norris Derivative filter segment length is 5 and segment spacing is 5. 3.2.3. Concentration prediction 70 sets of samples are selected as the sample set and 30 sets of samples are used as the prediction set. An analysis model is established, and the results obtained by different preprocess methods are shown in Table 2. The optimal spectral preprocess method is determined according to the correlation coefficient R, the root mean square error of correction (RMSEC) and root mean square error of prediction (RMSEP). It is generally believed that the closer the correlation coefficient R is to 1 and the root mean square error is closer to 0, the better the model is. Therefore, for the detection of C3F6, the best detection results can be obtained by directly quantifying the raw spectrum without processing. For the detection of CO, it is necessary to preprocess through the SavitzkyGolay filter. For the detection of C4F7N, the detection effect of the band overlapping with CO is better. Finally, the best detection scheme in Table 2 is used to quantitative analysis C4F7N, C3F6, and CO in the overheating experiment. The quantitative model of each component is shown in Fig. 5. The detection results are shown in Table 3. It can be seen that the CO concentration has a significant decrease at 500 °C, which has the same regularity as the absorbance analysis of C 3 F6 and COF2 in Section 3.1. However, the phenomenon is not observed by the
X. Zhang et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 233 (2020) 118161
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Fig. 4. 50 infrared spectral sample data in the 1700–1900 cm−1.
absorbance trend, which indicates that the rough subtractive spectrum is only suitable for qualitative judgment. The concentration of C4 F 7N is 6.35% at 350 °C due to the partial pressure method used in the inflation of the heating chamber, which may have some errors. As the temperature increased, the concentration of C 4F 7N generally decreased, but the decrease is small, indicating that the decomposition caused by high temperature is slower. The formation of the main decomposition product CO is observed to start at a temperature of approximately 650 °C in the thermal stability analysis of trace C4F7N [25]. It is similar to the results in this paper: with increasing temperature, and concentration can exceed 1000 ppm. In this paper, because the heating time is long and the concentration of C4F7N is higher, the possible decomposition temperature is lower and the concentration is higher. In addition, the results of C4F7N-N2 overheating decomposition experiment shows that the concentration of C3F6 is significantly higher than other decomposition products [24]. The highest concentration is close to 250 ppm, which indicates that C3F6 can be used as a feature parameter for overheating fault assessment.
In general, infrared spectroscopy and chemometric can be well used to predict the concentration of the decomposition products. It can not only contribute to the study of C4F7N decomposition characteristics but also can be applied to future equipment monitoring. Besides, as two main and detectable decomposition products, CO and C3F6 are biotoxic. Quick and easy quantification of the two decomposition products also has great significance for ensuring personal safety. 4. Conclusion In this paper, the C4F7N-CO2 gas mixture overheating decomposition is analysed by infrared spectroscopy. It is found that CO, C3F6, and COF2 can be effectively detected using the 4 m long optical path gas cell. Based on 100 randomly prepared samples of the gas mixture with decomposition products, the PLS analysis model is established to determine the optimal detection bands and preprocess method for CO, C 3F 6 and C4 F 7N. The concentrations of the three gases at different temperatures are predicted. The research
Table 2 Modeling results of different components under different preprocess method. Component
Band −1
C3F6
1776.15–1821.11 cm
C4F7N
1776.15–1821.11 cm−1
2102.03–2152.17 cm−1
CO
2102.03–2152.17 cm−1
Preprocess
R
RMSEC
RMSEP
Without preprocess First derivative Savitzky-Golay filter First derivative + Savitzky-Golay filter First derivative + Norris Derivative filter Without preprocess First derivative Savitzky-Golay filter First derivative + Savitzky-Golay filter First derivative + Norris Derivative filter Without preprocess First derivative Savitzky-Golay filter First derivative + Savitzky-Golay filter First derivative + Norris Derivative filter Without preprocess First derivative Savitzky-Golay filter First derivative + Savitzky-Golay filter First derivative + Norris Derivative filter
0.9974 0.9966 0.9971 0.9942 0.9963 0.7884 0.8102 0.8143 0.8025 0.7836 0.9663 0.9638 0.9624 0.9634 0.9563 0.9955 0.9985 0.9984 0.9984 0.9978
9.94 10.6 10.3 14.1 10.9 0.196 0.187 0.185 0.190 0.198 0.077 0.080 0.081 0.080 0.087 52.3 30.4 30.9 31.6 36.5
9.48 11.7 9.75 14.8 10.6 0.198 0.282 0.195 0.269 0.214 0.100 0.118 0.104 0.110 0.112 50.0 48.9 41.2 46.2 35.0
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X. Zhang et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 233 (2020) 118161
Table 3 Concentrations prediction based on PLS model at different temperatures.
Validation Calibration
Predicted value (ppm)
Temperature (°C)
CO (ppm)
C3F6 (ppm)
C4F7N (%)
350 400 450 500 550
61.73 103.04 517.85 409.46 1117.74
113.36 253.28 321.02 186.48 330.85
6.35 6.32 6.28 6.02 5.99
R=0.9974
CRediT authorship contribution statement
C3F6
Xiaoxing Zhang: Conceptualization, Validation, Resources, Writing review & editing, Supervision. Yin Zhang: Conceptualization, Methodology, Software, Writing - original draft, Supervision. Siyuan Zhou: Software, Formal analysis. Zhuo Wei: Data curation, Investigation.Yi Wang : Data curation, Investigation. Yufei Wang: Data curation, Investigation.
Actual value (ppm)
Declaration of competing interest
Validation Calibration
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Predicted value (ppm)
References
R=0.9985
CO
Actual value (ppm)
Validation Calibration
Predicted value (%)
R=0.9663
C4F7N
Actual value (%) Fig. 5. The quantitative model of each component.
provides new ideas for the monitoring of alternative gas-insulated equipment based on infrared spectroscopy in the future, which has good application potential.
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